A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance

Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rauch, Geraldine [verfasserIn] Kunzmann, Kevin - 1988- [verfasserIn] Kieser, Meinhard [verfasserIn] Wegscheider, Karl [verfasserIn] König, Jochem [verfasserIn] Eulenburg, Christine Helene - Gräfin zu, 1980- [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	clinical trials composite endpoint relevance weighting time‐to‐event

Anmerkung:	First published: 4 December 2017 Gesehen am 13.04.2018
Umfang:	19

Übergeordnetes Werk:	Enthalten in: Statistics in medicine - Chichester [u.a.] : Wiley, 1982, 37(2018), 5, Seite 749-767
Übergeordnetes Werk:	volume:37 ; year:2018 ; number:5 ; pages:749-767 ; extent:19

Links:	Volltext Volltext

DOI / URN:	10.1002/sim.7531

Katalog-ID:	1572000678

Internformat


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245	1	2	\|a A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance \|c Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg
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520			\|a Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example.
650		4	\|a clinical trials
650		4	\|a composite endpoint
650		4	\|a relevance weighting
650		4	\|a time‐to‐event
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912			\|a GBV_USEFLAG_U
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912			\|a SYSFLAG_1
912			\|a GBV_KXP
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_252
912			\|a GBV_ILN_266
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_647
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2018
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 37 \|j 2018 \|e 5 \|h 749-767 \|g 19
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982			\|2 2013 \|1 01 \|x DE-16-250 \|8 05 \|s s \|0 (DE-627)1410501914 \|a Verfasser
982			\|2 2013 \|1 01 \|x DE-16-250 \|8 05 \|s s \|a pos_ 2
982			\|2 2013 \|1 01 \|x DE-16-250 \|8 06 \|s p \|0 (DE-627)1450272401 \|a Kieser, Meinhard
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matchkey_str	article:10970258:2018----::wihecmieefcmaueotenlssfcmoieieoiseetnpitihopn
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allfields	10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3
spelling	10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3
allfields_unstemmed	10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3
allfieldsGer	10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3
allfieldsSound	10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3
language	English
source	Enthalten in Statistics in medicine 37(2018), 5, Seite 749-767 volume:37 year:2018 number:5 pages:749-767 extent:19
sourceStr	Enthalten in Statistics in medicine 37(2018), 5, Seite 749-767 volume:37 year:2018 number:5 pages:749-767 extent:19
format_phy_str_mv	Article
building	2013:0
institution	findex.gbv.de
selectbib_iln_str_mv	2013@01
topic_facet	clinical trials composite endpoint relevance weighting time‐to‐event
sw_local_iln_str_mv	2013:hd2018 DE-16-250:hd2018 2013:wissenschaftlicher Artikel (Zeitschrift) DE-16-250:wissenschaftlicher Artikel (Zeitschrift) 2013:per_6 DE-16-250:per_6 2013:s_19 DE-16-250:s_19 2013:Rauch, Geraldine DE-16-250:Rauch, Geraldine 2013:Medizinische Fakultät Heidelberg DE-16-250:Medizinische Fakultät Heidelberg 2013:Verfasser DE-16-250:Verfasser 2013:pos_1 DE-16-250:pos_1 2013:Kunzmann, Kevin DE-16-250:Kunzmann, Kevin 2013:Institut für Medizinische Biometrie und Informatik DE-16-250:Institut für Medizinische Biometrie und Informatik 2013:pos_ 2 DE-16-250:pos_ 2 2013:Kieser, Meinhard DE-16-250:Kieser, Meinhard 2013:pos_3 DE-16-250:pos_3
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container_title	Statistics in medicine
authorswithroles_txt_mv	Rauch, Geraldine @@aut@@ Kunzmann, Kevin @@aut@@ Kieser, Meinhard @@aut@@ Wegscheider, Karl @@aut@@ König, Jochem @@aut@@ Eulenburg, Christine Helene @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
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language_de	englisch
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title	A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance
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title_full	A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg
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title_sort	weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance
title_auth	A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance
abstract	Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. First published: 4 December 2017 Gesehen am 13.04.2018
abstractGer	Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. First published: 4 December 2017 Gesehen am 13.04.2018
abstract_unstemmed	Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. First published: 4 December 2017 Gesehen am 13.04.2018
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container_issue	5
title_short	A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance
url	http://dx.doi.org/10.1002/sim.7531 https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531
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author2	Kunzmann, Kevin 1988- Kieser, Meinhard Wegscheider, Karl König, Jochem Eulenburg, Christine Helene Gräfin zu 1980-
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GND_str_mv	Rauch, G. Rauch, Geraldine Kunzmann, Kevin Kieser, M. Kieser, Meinhard Wegscheider, Karl Koenig, Jochem König, Jochem Kleingarn, Christine Helene Eulenburg, Christine Eulenburg, Christine zu Eulenburg, Christine Helene zu
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GND_txtF_mv	Rauch, G. Rauch, Geraldine Kunzmann, Kevin Kieser, M. Kieser, Meinhard Wegscheider, Karl Koenig, Jochem König, Jochem Kleingarn, Christine Helene Eulenburg, Christine Eulenburg, Christine zu Eulenburg, Christine Helene zu
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doi_str	10.1002/sim.7531
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up_date	2024-07-04T20:40:20.377Z
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A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance

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